“Improved Feature Extraction and Classification Techniques for Multispectral Brain Magnetic Resonance Images”

Magnetic Resonance Imaging (MRI) is a multi sequence medical imaging technique in which stacks of images are acquired with different tissue contrasts. Simultaneous observation and quantitative analysis of normal brain tissues and small abnormalities from these large numbers of different sequences is a great challenge in clinical applications. Multispectral MRI analysis can simplify the job considerably by combining unlimited number of available co-registered sequences in a single suite. However, poor performance of the multispectral system with conventional image classification and segmentation methods makes it inappropriate for clinical analysis. Recent works in multispectral brain MRI analysis attempted to resolve this issue by improved feature extraction approaches, such as transform based methods, fuzzy approaches, algebraic techniques and so forth. Transform based feature extraction methods like Independent Component Analysis (ICA) and its extensions have been effectively used in recent studies to improve the performance of multispectral brain MRI analysis. However, these global transforms were found to be inefficient and inconsistent in identifying less frequently occurred features like small lesions, from large amount of MR data. The present thesis focuses on the improvement in ICA based feature extraction techniques to enhance the performance of multispectral brain MRI analysis. Methods using spectral clustering and wavelet transforms are proposed to resolve the inefficiency of ICA in identifying small abnormalities, and problems due to ICA over-completeness. Effectiveness of the new methods in brain tissue classification and segmentation is confirmed by a detailed quantitative and qualitative analysis with synthetic and clinical, normal and abnormal, data. In comparison to conventional classification techniques, proposed algorithms provide better performance in classification of normal brain tissues and significant small abnormalities.

Development of Techniques for the Automatic Extraction and Grade Detection of Glioma Tumors from Conventional Brain Magnetic Resonant Images

Cerebral glioma is the most prevalent primary brain tumor, which are classified broadly into low and high grades according to the degree of malignancy. High grade gliomas are highly malignant which possess a poor prognosis, and the patients survive less than eighteen months after diagnosis. Low grade gliomas are slow growing, least malignant and has better response to therapy. To date, histological grading is used as the standard technique for diagnosis, treatment planning and survival prediction. The main objective of this thesis is to propose novel methods for automatic extraction of low and high grade glioma and other brain tissues, grade detection techniques for glioma using conventional magnetic resonance imaging (MRI) modalities and 3D modelling of glioma from segmented tumor slices in order to assess the growth rate of tumors. Two new methods are developed for extracting tumor regions, of which the second method, named as Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA) can also extract white matter and grey matter from T1 FLAIR an T2 weighted images. The methods were validated with manual Ground truth images, which showed promising results. The developed methods were compared with widely used Fuzzy c-means clustering technique and the robustness of the algorithm with respect to noise is also checked for different noise levels. Image texture can provide significant information on the (ab)normality of tissue, and this thesis expands this idea to tumour texture grading and detection. Based on the thresholds of discriminant first order and gray level cooccurrence matrix based second order statistical features three feature sets were formulated and a decision system was developed for grade detection of glioma from conventional T2 weighted MRI modality.The quantitative performance analysis using ROC curve showed 99.03% accuracy for distinguishing between advanced (aggressive) and early stage (non-aggressive) malignant glioma. The developed brain texture analysis techniques can improve the physician’s ability to detect and analyse pathologies leading to a more reliable diagnosis and treatment of disease. The segmented tumors were also used for volumetric modelling of tumors which can provide an idea of the growth rate of tumor; this can be used for assessing response to therapy and patient prognosis.

Content Based Image Retrieval of Brain MR Images across Different Classes

Magnetic Resonance Imaging play a vital role in the decision-diagnosis process of brain MR images. For an accurate diagnosis of brain related problems, the experts mostly compares both T1 and T2 weighted images as the information presented in these two images are complementary. In this paper, rotational and translational invariant form of Local binary Pattern (LBP) with additional gray scale information is used to retrieve similar slices of T1 weighted images from T2 weighted images or vice versa. The incorporation of additional gray scale information on LBP can extract more local texture information. The accuracy of retrieval can be improved by extracting moment features of LBP and reweighting the features based on users’ feedback. Here retrieval is done in a single subject scenario where similar images of a particular subject at a particular level are retrieved, and multiple subjects scenario where relevant images at a particular level across the subjects are retrieved

Effect of thermal annealing on Fe40Ni38B18Mo4 thin films: modified Herzer model for magnetic evolution

Magnetic properties of nano-crystalline soft magnetic alloys have usually been correlated to structural evolution with heat treatment. However, literature reports pertaining to the study of nano-crystalline thin films are less abundant. Thin films of Fe40Ni38B18Mo4 were deposited on glass substrates under a high vacuum of ≈ 10−6 Torr by employing resistive heating. They were annealed at various temperatures ranging from 373 to 773K based on differential scanning calorimetric studies carried out on the ribbons. The magnetic characteristics were investigated using vibrating sample magnetometry. Morphological characterizations were carried out using atomic force microscopy (AFM), and magnetic force microscopy (MFM) imaging is used to study the domain characteristics. The variation of magnetic properties with thermal annealing is also investigated. From AFM and MFM images it can be inferred that the crystallization temperature of the as-prepared films are lower than their bulk counterparts. Also there is a progressive evolution of coercivity up to 573 K, which is an indication of the lowering of nano-crystallization temperature in thin films. The variation of coercivity with the structural evolution of the thin films with annealing is discussed and a plausible explanation is provided using the modified random anisotropy model

Large enhanced dielectric permittivity in polyaniline passivated core-shell nano magnetic iron oxide by plasma polymerization

Commercial samples of Magnetite with size ranging from 25–30nm were coated with polyaniline by using radio frequency plasma polymerization to achieve a core shell structure of magnetic nanoparticle (core)–Polyaniline (shell). High resolution transmission electron microscopy images confirm the core shell architecture of polyaniline coated iron oxide. The dielectric properties of the material were studied before and after plasma treatment. The polymer coated magnetite particles exhibited a large dielectric permittivity with respect to uncoated samples. The dielectric behavior was modeled using a Maxwell–Wagner capacitor model. A plausible mechanism for the enhancement of dielectric permittivity is proposed

Magnetic and topographical modifications of amorphous Co–Fe thin films induced by high energy Ag7+ ion irradiation

We have investigated the effects of swift heavy ion irradiation on thermally evaporated 44 nm thick, amorphous Co77Fe23 thin films on silicon substrates using 100 MeV Ag7+ ions fluences of 1 1011 ions/ cm2, 1 1012 ions/cm2, 1 1013 ions/cm2, and 3 1013 ions/cm2. The structural modifications upon swift heavy irradiation were investigated using glancing angle X-ray diffraction. The surface morphological evolution of thin film with irradiation was studied using Atomic Force Microscopy. Power spectral density analysis was used to correlate the roughness variation with structural modifications investigated using X-ray diffraction. Magnetic measurements were carried out using vibrating sample magnetometry and the observed variation in coercivity of the irradiated films is explained on the basis of stress relaxation. Magnetic force microscopy images are subjected to analysis using the scanning probe image processor software. These results are in agreement with the results obtained using vibrating sample magnetometry. The magnetic and structural properties are correlated

Structural, topographical and magnetic evolution of RF-sputtered Fe-Ni alloy based thin films with thermal annealing

Metglas 2826 MB having a nominal composition of Fe40Ni38Mo4B18 is an excellent soft magnetic material and finds application in sensors and memory heads. However, the thin-film forms of Fe40Ni38Mo4B18 are seldom studied, although they are important in micro-electro-mechanical systems/nano-electromechanical systems devices. The stoichiometry of the film plays a vital role in determining the structural and magnetic properties of Fe40Ni38Mo4B18 thin films: retaining the composition in thin films is a challenge. Thin films of 52 nm thickness were fabricated by RF sputtering technique on silicon substrate from a target of nominal composition of Fe40Ni38Mo4B18. The films were annealed at temperatures of 400 °C and 600 °C. The micro-structural studies of films using glancing x-ray diffractometer (GXRD) and transmission electron microscope (TEM) revealed that pristine films are crystalline with (FeNiMo)23B6 phase. Atomic force microscope (AFM) images were subjected to power spectral density analysis to understand the probable surface evolution mechanism during sputtering and annealing. X-ray photoelectron spectroscopy (XPS) was employed to determine the film composition. The sluggish growth of crystallites with annealing is attributed to the presence of molybdenum in the thin film. The observed changes in magnetic properties were correlated with annealing induced structural, compositional and morphological changes